Multi data process switching for nanoparticle baseline and detection threshold determination

ABSTRACT

Systems and methods are described for automatically utilizing multiple data processing methods on a given spectrometry dataset for the determination of nanoparticle detection factors including nanoparticle baseline and detection threshold.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application claims the benefit of 35 U.S.C. § 119(e) of U.S.Provisional Application Ser. No. 63/335,510, filed Apr. 27, 2022, andtitled “NANOPARTICLE BASELINE AND PARTICLE DETECTION THRESHOLDDETERMINATION THROUGH ITERATIVE OUTLIER REMOVAL,” of U.S. ProvisionalApplication Ser. No. 63/335,516, filed Apr. 27, 2022, and titled“NANOPARTICLE DETECTION THRESHOLD DETERMINATION THROUGH LOCAL MINIMUMANALYSIS,” and of U.S. Provisional Application Ser. No. 63/335,523,filed Apr. 27, 2022, and titled “MULTI DATA PROCESS SWITCHING FORNANOPARTICLE BASELINE AND DETECTION THRESHOLD DETERMINATION.” U.S.Provisional Applications Ser. Nos. 63/335,510, 63/335,516, and63/335,523 are herein incorporated by reference in their entireties.

BACKGROUND

Inductively coupled plasma (ICP) mass spectroscopy is an analysistechnique commonly used for the determination of trace elementconcentrations and isotope ratios in liquid samples. ICP massspectroscopy employs electromagnetically generated partially ionizedargon plasma which reaches a temperature of approximately 7000K. When asample is introduced to the plasma, the high temperature causes sampleatoms to become ionized or emit light. Since each chemical elementproduces a characteristic mass or emission spectrum, measuring saidspectra allows the determination of the elemental composition of theoriginal sample.

Sample introduction systems may be employed to introduce the liquidsamples into the ICP mass spectroscopy instrumentation (e.g., aninductively coupled plasma mass spectrometer (ICP/ICPMS), an inductivelycoupled plasma atomic emission spectrometer (ICP-AES), or the like) foranalysis. For example, a sample introduction system may withdraw analiquot of a liquid sample from a container and thereafter transport thealiquot to a nebulizer that converts the aliquot into a polydisperseaerosol suitable for ionization in plasma by the ICP mass spectrometryinstrumentation. The aerosol is then sorted in a spray chamber to removethe larger aerosol particles. Upon leaving the spray chamber, theaerosol is introduced to the ICPMS or ICPAES instruments for analysis.Often, the sample introduction is automated to allow a large number ofsamples to be introduced into the ICP mass spectroscopy instrumentationin an efficient manner.

SUMMARY

Systems and methods for analyzing spectrometry data for thedetermination of nanoparticle factors including one or more ofnanoparticle baselines and nanoparticle detection thresholds aredescribed. In an aspect, a method embodiment includes, but is notlimited to, transferring a fluid sample containing nanoparticles to aspectrometry sample analyzer; generating a spectrometry data set via thespectrometry sample analyzer associated with detected ion signalintensity over time; generating from the spectrometry data set, via oneor more computer processors, a raw data set that includes a countdistribution of counts of ion signal intensity and a frequency of theion signal intensity of each count; iteratively removing, via the one ormore computer processors, ion signal intensity values that exceed anoutlier threshold value associated with a sum of a first multiple of anaverage of the count distribution of ion signal intensity and a firstmultiple of a standard deviation of the count distribution of ion signalintensity until no count values exceed the outlier threshold value toprovide a background data set; and setting, via the one or more computerprocessors, a nanoparticle baseline intensity value as a sum of a secondmultiple of an average of the background data set and a second multipleof a standard deviation of the background data set, wherein the firstmultiple of the standard deviation of the count distribution of ionsignal intensity differs from the second multiple of a standarddeviation of the background data set.

In an aspect, a method embodiment includes, but is not limited to,transferring a fluid sample containing nanoparticles to a spectrometrysample analyzer; generating a spectrometry data set via the spectrometrysample analyzer associated with detected ion signal intensity over time;forming, via one or more computer processors, a histogram of thespectrometry data set, the histogram associated with a frequency ofcounts of integrated ion signal intensity values; incrementing along thehistogram, via the one or more computer processors, a window spanningmultiple counts of the histogram to determine a potential local minimumfrequency value within the window; validating, via the one or morecomputer processors, whether the potential local minimum frequency valueis a local minimum for the histogram to provide a validated localminimum; and assigning, via the one or more computer processors, thevalidated local minimum as a detection threshold for nanoparticles inthe spectrometry data set.

In an aspect, a method embodiment includes, but is not limited to,transferring a fluid sample containing nanoparticles to a spectrometrysample analyzer; generating a spectrometry data set via the spectrometrysample analyzer associated with detected ion signal intensity over time;analyzing the spectrometry data set with a first data process, via oneor more computer processors, to determine at least one of a firstnanoparticle baseline or a first nanoparticle detection threshold withthe first data process; automatically switching to a second data processto analyze, via the one or more computer processors, the spectrometrydata set to determine at least one of a second nanoparticle baseline ora second nanoparticle detection threshold with the second data process;and determining, via the one or more computer processors, whetherresults from the first data process converge or diverge from resultsfrom the second data process.

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

DRAWINGS

The Detailed Description is described with reference to the accompanyingfigures.

FIG. 1A is a schematic illustration of a system for analysis ofnanoparticles in accordance with example implementations of the presentdisclosure.

FIG. 1B is a partial diagrammatic illustration of the system of FIG. 1Ain accordance with example implementations of the present disclosure.

FIG. 2 is a schematic illustration of a spectrometry data set shown witha normal distribution curve in accordance with example implementationsof the present disclosure.

FIG. 3 is a flow diagram of a process for iterative determination ofoutlier data from a spectrometry data set to determine particle baselineand a detection threshold for nanoparticles in accordance with exampleimplementations of the present disclosure.

FIG. 4 is a flow diagram of the process of FIG. 3 , showing an exampleiteration step.

FIG. 5 is a schematic illustration of an example dataset of theiteration step of the process of FIG. 3 .

FIG. 6 is a schematic illustration of a first iteration of the processof FIG. 3 to remove a first portion of outlier data.

FIG. 7 is a schematic illustration of a second iteration of the processof FIG. 3 to determine whether any outlier data remains.

FIG. 8 is a schematic illustration of a particle baseline determinationof the process of FIG. 3 following outlier removal.

FIG. 9A is a diagram of example datasets analyzed in accordance withexample implementations of the present disclosure.

FIG. 9B is a diagram showing a close-up of the data from FIG. 9A withthe determined particle baseline shown over the spectrometry data.

FIG. 10 is a schematic illustration of a spectrometry data set shownwith an approximated background determination for determination ofnanoparticles in accordance with example implementations of the presentdisclosure.

FIG. 11 is a flow diagram of a process for local minimum determinationand validation from a spectrometry data set to determine a detectionthreshold for nanoparticles in accordance with example implementationsof the present disclosure.

FIG. 12A is a schematic illustration of an example window used todetermine potential minimum data points from a histogram of thespectrometry data.

FIG. 12B is a schematic illustration of an example window used todetermine potential minimum data points from a histogram of thespectrometry data.

FIG. 13 is a schematic illustration of determining whether data pointsfrom a histogram of the spectrometry data are potential local minimumdata points for an example window size.

FIG. 14 is a schematic illustration of validating potential localminimum data points from a histogram of the spectrometry data arepotential local minimum data points for an example window size.

FIG. 15 is a diagram of example datasets analyzed in accordance withexample implementations of the present disclosure, with a detectionthreshold for nanoparticles shown.

FIG. 16 is a schematic illustration of a method for multi data processswitching in accordance with example implementations of the presentdisclosure.

FIG. 17 is a schematic illustration of the method of FIG. 16 withmultiple data processes having convergent data results.

FIG. 18 is a schematic illustration of the method of FIG. 16 withmultiple data processes having divergent data results.

FIG. 19 is a schematic illustration of the method of FIG. 16 withmultiple data processes having convergent data results and multiple dataprocesses having divergent data results.

DETAILED DESCRIPTION Overview

Nanoparticle research has grown to encompass applications from themedical industry to the environmental industry. Such applications canfocus on capabilities to detect nanoparticles (e.g., particles of lessthan 1000 nm in diameter) and to calculate the sizes of nanoparticlespresent in a sample. However, determining what is a nanoparticle andwhat is not a nanoparticle when analyzing spectrometry data poses manychallenges. For instance, spectrometry data, such as ICPMS data,includes information associated with ionized samples and backgroundinterference, such as resulting from plasma gases introduced to the ICPtorch, that can overlap with data associated with small nanoparticles.For example, as the size of the nanoparticle decreases, the spectrometrydata of the nanoparticle begins to converge with data associated withionic species produced by the ICP torch. This overlap and the associatedchallenges with removing background interferences, while avoidingnanoparticle data removal, lead to continued problems in providingreliable data associated with nanoparticles, including, but not limitedto, identification of nanoparticles and determining the number ofnanoparticles and their associated size distributions.

Accordingly, in one aspect, the present disclosure is directed tosystems and methods for automatically utilizing multiple data processingmethods on a given spectrometry dataset for the determination ofnanoparticle detection factors, such as nanoparticle baseline anddetection threshold. As the results of each data processing methodconverge towards a similar result for one or more of nanoparticlebaseline and detection threshold, there is a higher probability of areliable result. As the results of each data processing method divergeaway from a similar result for one or more of nanoparticle baseline anddetection threshold, there is a lower probability of a reliable result.In various aspects, convergence of a majority group of data processingmethods can be used to discount or otherwise marginalize the results ofa minority group of data processing methods.

Example Implementations

Referring generally to FIGS. 1A through 19 , a process is shown forutilizing multiple data processes for the determination of nanoparticlebaseline and nanoparticle detection thresholds in accordance withexample implementations of the present disclosure. The process canswitch between multiple data processes to analyze one or more propertiesof a spectrometry dataset and compare the results of the multiple dataprocesses to determine a probability that one or more of the multipledata processes is providing reliable results for the determination ofnanoparticle baseline and nanoparticle detection thresholds. The instantdisclosure provides description of an example system for analysis ofnanoparticles in FIGS. 1A and 1B, of example data processes in FIGS. 2through 14 , with an iterative determination data process described withrespect to FIGS. 2 through 9B and a local minimum data process describedwith respect to FIGS. 10 through 15 , and a description of exampleswitching between multiple data processes with respect to FIGS. 16through 19 .

Referring to FIGS. 1A and 1B, a system 100 for analysis of nanoparticlescontained in fluid samples is shown in accordance with exampleimplementations of the present disclosure. The system 100 generallyincludes a sample source 102, an inductively coupled plasma (ICP) torch104, a sample analyzer 106, and a controller 108. The sample source 102supplies a fluid sample containing nanoparticles for analysis by thesample analyzer 106 and can include, for example, an autosampler (e.g.,autosampler 110 shown in FIG. 1B) to automate fluid handling of thesample. For instance, the autosampler 110 manipulates a sample probe 112to draw fluid samples held in fluid containers 114 (e.g., sample vials,sample bottles, etc.) and transfer the fluid samples from theautosampler to other portions of the system, such as through vacuumtransfer, pump transfer, or the like. The samples can include fluidscontaining nanoparticles of interest, diluents, sample matrixcomponents, components for generation of calibration curves (e.g.,standard fluids, standard nanoparticles, etc.), or the like, orcombinations thereof. In implementations, the controller 108 facilitatescontrol of one or more aspects of the fluid transfer from theautosampler 110. In implementations, the controller 108 includes acomputer processor communicatively coupled with a computer memory toaccess control programming associated with one or more processesdescribed herein for execution by the computer processor.

The sample source 102 is fluidically coupled with the ICP torch 104(e.g., via a fluid transfer line 116) to transfer the fluid samplecontaining nanoparticles to the ICP torch 104 for ionization of thesample for analysis by the sample analyzer 106. In implementations, thesample source 102 includes one or more sample conditioning systems toprepare the fluid sample for introduction to the ICP torch 104. Forexample, the sample source 102 can include a nebulizer to receive thefluid sample from the autosampler 110 and aerosolize the fluid sampleand a spray chamber to receive the aerosolized sample from the nebulizerand remove larger aerosol components through impact against spraychamber walls. The sample source 102 can thus condition the fluid sampleto promote substantially continuous operation of the ICP torch 104 forsample ionization, such as by aerosolizing the sample and removinglarger aerosol components to prevent extinguishing of the plasmagenerated by the ICP torch 104.

An example ICP torch 104 is shown in FIG. 1B, where the system is shownincluding a plasma torch assembly 118, a radio frequency (RF) inductioncoil 120 that is coupled to an RF generator (not shown), and aninterface 122. The plasma torch assembly 118 includes a housing 124 thatreceives a plasma torch 126 configured to sustain the plasma. The plasmatorch 126 is shown including a torch body 128, a first (outer) tube 130,a second (intermediate) tube 132, and an injector assembly 134 whichincludes a third (injector) tube 136. The plasma torch 126 is mounted bythe housing 124 for positioning centrally in the RF induction coil 120so that the end of the first (outer) tube 130 is adjacent to (e.g.,approximately 10-20 mm from) the interface 122. The interface 122, whichcan be included in the sample analyzer 106 or as a separate componentthereof, generally includes a sampler cone 138 positioned adjacent tothe plasma and a skimmer cone 140 positioned adjacent to the samplercone 138, opposite the plasma. A small diameter opening 142, 144 isformed in each cone 138, 140 at the apex of the cone 138, 140 to allowthe passage of ions from the inductively coupled plasma for analysis bythe sample analyzer 106.

A flow of gas (e.g., the plasma-forming gas), which is used to form theplasma (e.g., plasma 146), is passed between the first (outer) tube 130and the second (intermediate) tube 132. A second flow of gas (e.g., theauxiliary gas) is passed between the second (intermediate) tube 132 andthe third (injector) tube 136 of the injector assembly 134. The secondflow of gas can be used to change the position of the base of the plasmarelative to the ends of the second (intermediate) tube 132 and the third(injector) tube 136. In implementations, the plasma-forming gas and theauxiliary gas include argon (Ar), however other gases may be usedinstead of or in addition to argon (Ar), in specific implementations.The RF induction coil 120 surrounds the first (outer) tube 1130 of theplasma torch 126. RF power (e.g., 750-1500 W) is applied to the coil 120to generate an alternating current within the coil 120. Oscillation ofthis alternating current (e.g., 27 MHz, 40 MHz, etc.) causes anelectromagnetic field to be created in the plasma-forming gas within thefirst (outer) tube 130 of the plasma torch 126 to form an ICP dischargethrough inductive coupling. A carrier gas is then introduced into thethird (injector) tube 136 of the injector assembly 134. The carrier gaspasses through the center of the plasma, where it forms a channel thatis cooler than the surrounding plasma. Samples to be analyzed areintroduced into the carrier gas for transport into the plasma region,where the samples can be formed into an aerosol of liquid by passing theliquid sample from the sample source 102 into a nebulizer. As a dropletof nebulized sample enters the central channel of the ICP, it evaporatesand any solids that were dissolved or carried in the liquid vaporize andthen break down into atoms. In implementations, the carrier gas includesargon (Ar), however, other gases may be used instead of, or in additionto, argon (Ar) in specific implementations.

The sample analyzer 106 generally includes a mass analyzer 148 and anion detector 150 to analyze the ions received from the ICP torch 104.For example, the sample analyzer 106 can direct ions received from theplasma of the ICP torch 104 and directed through the cones 138, 140 tothe mass analyzer 148. The sample analyzer 106 can include various ionconditioning components, including, but not limited to, ion guides,vacuum chambers, reaction cells, and the like, suitable for operation ofan ICPMS system. The mass analyzer 148 separates ions based on differingmass to charge ratios (m/z). For instance, the mass analyzer 148 caninclude a quadrupole mass analyzer, a time of flight mass analyzer, orthe like. The ion detector 150 receives the separated ions from the massanalyzer 148 to detect and count ions according to the separated m/zratios and output a detection signal. The controller 108 can receive thedetection signal from the ion detector 150 to coordinate data fordetermination of the concentration of components in the ionized sampleaccording to intensity of the signals of each ion detected by the iondetector 150 and for the determination of nanoparticle characteristicsfor nanoparticles contained in the fluid sample (e.g., nanoparticlesize, nanoparticle amount, etc.).

An example spectroscopy data set from the controller 108 is shown inFIG. 2 , where a spectrometry data set 200 is shown with a normaldistribution curve 202. Ionic content present in a sample analyzed byICPMS is generally homogenous. The processes described herein canproceed as though the ionic signals resemble a normal distributioncentered around the average signal. For example, in the examplespectrometry data set 200, ionic signals within one standard deviationfrom the average (i.e., μ±σ) account for 68.27% of all the ionicsignals, whereas ionic signals within two standard deviations from theaverage (i.e., μ±2σ) account for 95.45% of all the ionic signals, andionic signals within three standard deviations from the average (i.e.,μ±3σ) account for 99.73% of all the ionic signals. Outliers from thedistribution are potentially nanoparticles present in the sampleanalyzed by the ICPMS. However, outliers can skew the standard deviationof the spectrometry dataset, so the processes described hereiniteratively remove outliers from the dataset. For instance, exampleprocesses are described herein that iteratively remove outlier data fromspectrometry data sets to determine particle baseline and a detectionthreshold for nanoparticles in accordance with example implementationsof the present disclosure.

Referring to FIG. 3 , a flow diagram is shown of a process 300 foriterative determination of outlier data from a spectrometry data set todetermine particle baseline and a detection threshold for nanoparticlesin accordance with example implementations of the present disclosure.The flow diagram begins with a raw data set provided throughspectrometry analysis of a sample (e.g., via ICPMS) in block 302. Forexample, for a spectrometry data set including ion signal intensitydetected by the ion detector 108 as a function of time, the raw data setcan include a count distribution of ion signal intensity and a frequencyof the ion signal intensity. The raw data set is processed to determinea first iteration of an average and a standard deviation to determineoutlier data points (e.g., those above a threshold) in block 304. In theexample process shown, the outlier data points are identified as thoseexceeding a threshold of 1*avg+5*standarddeviation (1 μ+5σ). It is notedthat the processes described herein are not limited to the multiplesprovided in the threshold calculation (i.e., 1*avg or5*standarddeviation), where different multiples for the average and thestandard deviation can be utilized. For example, in implementations, themultiples for the average and the standard deviation are auser-selectable feature. For instance, a user can select a specificmultiple for the average and the standard deviation via interaction witha user interface communicatively coupled with the system 100.

The process 300 then removes any outliers from the data set based on theprevious threshold calculation (i.e., 1 μ+5σ) to approach a data sethaving no outliers (i.e., only ionic data without nanoparticle data) inblock 306. The remaining data set (i.e., the raw data set without theoutlier data) is then processed to determine a second iteration of anaverage and a standard deviation of the remaining data set to determineoutlier data points (e.g., those above a threshold). For example, theprocess 300 proceeds to block 308 to determine whether any outliersremain based on a new threshold calculation with the remaining datasetafter removal of the outlier datapoints from block 306. If outlier datapoints remain (i.e., “Yes” at block 308), the process 300 canacknowledge a data set having non-particle data still present in block310 for further iterative removal of particle data. The process 300continues to iterate the data set to remove the outlier data until nofurther outliers are identified. For example, the process 300 canproceed back to block 304 to treat the data from block 310 instead ofthe raw data set from block 302. When no further outliers areidentified, the process establishes the resultant dataset as the databackground and determines the nanoparticle baseline based on the databackground in block 312. In implementations, the data background isdetermined using a baseline calculation having one or more differentmultiples than used for the iterative threshold calculations. Forexample, while the threshold is shown as aμ+bσ, the baseline calculationis shown as xμ_(background)+yσ_(background), as described furtherherein. An example of the process 300 is described with respect to FIGS.4-8

Referring to FIGS. 4-6 , an initial iterative step for treatment of theraw data set is shown, where FIG. 5 shows an example raw dataset fromblock 302 provided in simplified form for initial outlier removal. Asshown in FIG. 6 , the outlier threshold is determined to be 3.7 based ona threshold calculation of 1 μ+5σ. Data exceeding the 3.7 cutoff isidentified as outliers and subsequently removed from the dataset forfurther iterations. For example, the replicates shown exceeding thethreshold line of 3.7 (i.e., those extending near 5) are removed fromthe dataset in block 306. FIG. 7 shows a second iteration illustratingthe dataset with the datapoints that exceeded the threshold line of 3.7removed. In the second iteration, the new outlier threshold isdetermined to be 2.0 based on the same threshold calculation of 1 μ+5σ,wherein no datapoints are determined to be outliers, since no datapointsexceed 2.0.

When no outliers are present, the process 300 determines that thenanoparticle outliers have been removed from the dataset, such that ananoparticle baseline determination can be made. The process 300 thenmoves to block 312 to determine the nanoparticle baseline based on thedata background. For example, FIG. 8 shows the resultant data providingthe data background and determines the nanoparticle baseline based onthe data background. While the particle baseline formula shown includes1*avg_(background)+3.3*standarddeviation_(background) the process is notlimited to such values, where different multiples for the average andthe standard deviation can be utilized. For example, in implementations,the multiples for the average and the standard deviation are auser-selectable feature. In implementations, the multiples for theaverage and the standard deviation can be different than the multiplesfor the average and the standard deviation during the iterative removalsteps of process 300 (e.g., during block 304). For example, themultiples for the average and the standard deviation for the particlebaseline calculation as 1 and 3.3, respectively, whereas the multiplesfor the average and the standard deviation for the iterative removalcalculation as 1 and 5, respectively. Differing multiples for thestandard deviation can provide different levels of strictness indetermining what data is considered below or above the particle baselinefollowing determination of the background datasets. In implementations,the process 300 can remove zero values for the datasets.

Referring to FIGS. 9A and 9B, example datasets are shown with theiterative step including a formula of 1*avg+5*standarddeviation and theparticle baseline formula of1*avg_(background)+1*standarddeviation_(background). FIG. 9B illustratesa subset of the data from FIG. 9A with the determined particle baseline900 shown over the spectrometry data 902.

Referring to FIGS. 10 through 15 , a local minimum data process isdescribed for the determination of a detection threshold ofnanoparticles. FIG. 10 shows an example spectrometry data set with anapproximated background determination for the detection of nanoparticleswhere the portion of the data set attributable to signal background(shown as 1000, e.g., attributable to background interference, such asresulting from ionized plasma gases from the ICP torch) is separatedfrom data set attributable to nanoparticles (shown as 1002). Thenanoparticle detection threshold represents the transition from thebackground portion 1000 to the nanoparticle portion 1002, where thenanoparticle detection threshold provides a data boundary where datapoints involving intensities greater than the nanoparticle detectionthreshold can be treated as originating from nanoparticles present inthe sample.

Referring to FIG. 11 , a flow diagram is shown of a process 1100 forlocal minimum determination and validation of a spectrometry data set todetermine a detection threshold for nanoparticles in accordance withexample implementations of the present disclosure. The flow diagrambegins with a raw data set being manipulated to remove background and tointegrate contiguous data points in block 1102. In implementations, theraw data set includes an intensity over time data set provided by anICPMS, where the background to be removed from the raw data set isdetermined through a data process, such as the iterative determinationdata process described with respect to FIGS. 2 through 9B, is auser-selected feature, or combinations thereof. In implementations, thedata set is integrated following removal of the background from the rawdata set. For example, time-consecutive non-zero data points fordetected intensity are summed together, where the data points can beconsidered to be time-consecutive when no intervening zero value isdetected by the ICPMS for a given time detection interval, such as adetection interval of 0.01 secs. By integrating after backgroundremoval, more zero data points can be present since the backgroundremoval can filter out lower non-zero data points from the raw data setto provide zero values.

The process 1100 then continues to block 1104 where a histogram of themanipulated data set is formed. In implementations, the histogram isformed by rounding all integrated data points to the nearest integercount value and determining the frequency for each rounded point (e.g.,a value of 3.2 is rounded to a value of 3, whereas a value of 3.7 isrounded to 4). In implementations, the data is rounded down to the nextinteger count value (e.g., each of 3.2 and 3.7 is rounded down to avalue of 3). In implementations, the data is rounded up to the nextinteger count value (e.g., each of 3.2 and 3.7 is rounded up to a valueof 4). The histogram can be formed from the rounded points based on howmany of each point is present (e.g., the frequency of occurrence of eachcount). Example histograms of simplified data sets are shown withrespect to FIGS. 12A through 14 .

The process 1100 further includes examining frequencies of the histogrambased on a window size for the counts to determine potential localminimum count values in block 1106. In implementations, the window sizeis an odd number (e.g., a window covering five counts), where the centervalue for the window is compared against values to the left and to theright of the center position on the histogram to determine whether alocal minimum count is present (e.g., whether the frequency of the countat the center of the window is less than the frequencies of the countsto the left and to the right of the center count based on the windowsize). For counts at the beginning edge of the histogram, (e.g., counts0, 1, 2, etc.), the window may be contracted by not extending over thefull window size. For example, FIG. 11A shows a window 1200 coveringcounts 1 through 4 (i.e., a window size of four), where a frequency of26, count 2 is reviewed for determining whether the frequency of 26,count 2 is the potential minimum for the given window. The window couldbe considered as covering count 0 to the left of count 1, such thatcount 2 is positioned at the center of the window 1200 having a windowsize of five counts, however no data exists for count 0, so the windowcovers those counts that are present in the histogram. Similarly, priorto considering count 2, count 1 would be reviewed for a potential localminimum, where if count 1 was the center position of the window 1200,the frequency of 51 would be compared against the frequency of 26, count2 and the frequency of 12, count 3 to determine whether 51 is the localminimum, where it would be determined that it is not a local minimum.

The process 1100 determines whether the center frequency value of thewindow is a local minimum in block 1108. If the center frequency valueis not a local minimum, the process 1100 proceeds to block 1110 wherethe window is incremented further to the right of the histogram toreview additional count ranges to determine whether the new centerfrequency value is a local minimum (e.g., via blocks 1106 and 1108). Forexample, the frequency of 26, count 2 from FIG. 12A is not a localminimum, since the frequency of 12, count 3 and the frequency of 5,count 4 are each less than 26. The window 1200 would then be moved to becentered above count 3 to evaluate whether the frequency of 12 is alocal minimum as compared against the frequencies of count 1, count 2,count 4, and count 5. The frequency of 12, count 3 would not be thelocal minimum, since the frequency of 5, count 4 and the frequency of 2,count 5 are each less than 12. The window 1200 would then be moved to becentered above count 4 to evaluate whether the frequency of 5 is a localminimum as compared against the frequencies of count 2, count 3, count5, and count 6.

The process 1100 would continue to evaluate each new iteration of theplacement of the window 1200. For example, FIGS. 12B and 13 show thewindow 1200 further down the histogram as compared to FIG. 12A, wherethe window 1200 covers counts 3 through 7 (i.e., a window value of 5),to determine whether a frequency of 2 would be a potential minimum forthe given window. Since the window placement includes 0 frequency valuesat counts 6 and 7 within the window 1200, the process 1100 would notidentify a frequency of 2 as a potential minimum. The process 1100 wouldcontinue until a potential local minimum is identified. For example FIG.13 shows the window incrementing to the right of the histogram untilcentered above frequency 0, count 6 which is identified as the potentialminimum.

When a potential minimum is identified in block 1108, the process 1100continues to block 1112, where the process 1100 validates whether thepotential minimum is a validated minimum. If the potential minimum isnot validated, process 1100 continues back to block 1110 to incrementthe window to be centered above the next count. If the potential minimumis validated in block 1112, the process 1100 would identify the localminimum as a threshold value for nanoparticle detection in block 1114.For example, referring to FIG. 14 , the frequency value of 2 in thehistogram is first identified as a potential minimum, since 2 is lessthan the frequency value of the remainder of the values in the window1200 (i.e., 2 is less than 22, 18, and 15).

The process 1100 then determines whether that potential minimum isvalidated. In implementations, to determine if a local minimum is valid,the average value of all the frequencies within the window is calculatedto determine whether the potential minimum is within one standarddeviation from the window average. In implementations, the validationcan include determining whether the potential minimum is within amultiple of the standard deviation from the window average. If thepotential minimum is more than one standard deviation from the windowaverage, then the potential minimum is not validated as a minimum value.For example, referring to FIG. 14 , the frequency value of 2, count 2 isnot validated, since the frequency value of 2 is not within one standarddeviation (shown as 8.65) of the window average (14.25). For instance, 2+8.65 is less than 14.25, so the frequency value of 2, count 2 is notvalidated.

Continuing with the example shown in FIG. 14 , the window place isincremented down the histogram until centered above count 6, where thefrequency of 0 is determined to be a potential minimum. The frequency of0 is within one standard deviation (6.34) of the window average (3.8),thereby validating count 6, frequency 0 to be the local minimum. Thevalidated localized minimum in then used as the detection threshold fornanoparticles, where intensities greater than the detection thresholdare treated as nanoparticles and intensities lower than the detectionthreshold are treated as background (e.g., ionic samples, massspectrometry interference, nanoparticles of a size below the detectionthreshold). FIG. 15 shows an example dataset analyzed according to thelocal minimum analysis process, with a detection threshold 1500illustrated to separate background (i.e., intensity values preceding thedetection threshold 1500) from intensity values corresponding tonanoparticles present in the sample (i.e., intensity values followingthe detection threshold 1500).

Referring to FIGS. 16 through 19 , a process 1600 is shown for utilizingmultiple data processes for the determination of nanoparticle baselineand nanoparticle detection thresholds in accordance with exampleimplementations of the present disclosure. The system 100 can utilizethe process 1600 to analyze a single data set from the sample analyzer106 according to multiple data processes to compare the results againsteach other (e.g., via the controller 108), such as to determineconverging or diverging data results from the multiple data processes.The process 1600 can include analyzing a spectrometry data set 1602 withmultiple data processes (e.g., data processes 1604, 1606, 1608) toachieve multiple data results, where the multiple data processes areautomatically switched from one data process to the next data process toanalyze the same spectrometry data set according to the multiple dataprocesses. For instance, the controller 108 can automate analysis of thespectrometry data set 1602 according to one data process, then switch toanalyze the initial spectrometry data set 1602 according to one or moredifferent analyses to determine whether the results of the dataprocesses are reliable or too divergent. For example, the process 1600is shown with a first data process 1604, a second data process 1606, upto n data processes 1608 used to process the same spectrometry data set(e.g., spectrometry data set 1602) to provide an indication ofreliability of the results of one or more of the data processes, where ncan be any number greater than 2. In implementations, the controller 108of the system 100 facilitates analysis of the spectrometry data set 1602according to one or more of the data processes of process 1600. Forexample, the controller 108 can include or be communicatively coupledwith a computer memory device that stores one or more data algorithms,programs, or other processes to generate data results according to oneor more of the data processes of process 1600.

The data processes (e.g., data processes 1604, 1606, 1608) can include,but are not limited to, the iterative determination data processdescribed with respect to FIGS. 2 through 9B, the local minimum dataprocess described with respect to FIGS. 10 through 15 , an instrumentspecific data analysis process (e.g., ICPMS analysis instrumentsoftware), a user defined data process having user-configurable featuresand/or user-defined values (e.g., facilitating a user to select areference material, determine what particle baseline and detectionthreshold are to be used for the reference material, and apply theparticle baseline and detection threshold for future samples), or otherdata processes. Each of the data processes produces a data resultfollowing processing of the original spectrometry data set. For example,the first data process 1604 is shown producing a first set of dataresults 1610, the second data process 1606 is shown producing a secondset of data results 1612, and the third data process 1608 is shownproducing a third set of data results 1614. Example data resultsinclude, but are not limited to, a particle baseline 1616, ananoparticle detection threshold 1618, a number of particles 1620, aparticle size and standard deviation 1622, and the like, andcombinations thereof.

In implementations, each data process can provide one or more categoriesof data results, which can be the same categories or differentcategories than the other data processes used to analyze thespectrometry data set. For example, a first data process (e.g., dataprocess 1604) can provide data results associated with a particlebaseline and a detection threshold, a second data process (e.g., dataprocess 1606) can provide data results associated with a detectionthreshold (and not a particle baseline), a third data process (e.g., adata process between data process 1606 and data process 1608) canprovide data results associated with a particle baseline, a detectionthreshold, and a number of particles, and a fourth data process (e.g.,data process 1608) can provide data results associated with a particlebaseline, a detection threshold, a number of particles, and a particlesize and standard deviation. The data results can help establishinformation associated with mass spectrometer interference, ionicmaterial measurements, particles below the detection threshold, and soforth.

The multiple data processes can be utilized to determine a probabilitythat the data result from any one or more of the data processes is areliable result or an unreliable result. For example, referring to FIG.17 , each of the data processes is shown providing convergent dataresults (e.g., shown as 1700) which provides a high confidence levelthat the results of each of the data processes is reliable. Determiningwhether the results of the data processes are convergent or divergent(e.g., shown as 1702) can occur through statistical models, such asdetermining whether a data process result is excluded from a standarddeviation analysis of the total results, or through another model. Forexample, each of the data processes can output data results associatedwith a nanoparticle detection threshold, where the detection thresholdof each data process is within a statistical similarity to indicate ahigh probability of a reliable nanoparticle detection threshold.Referring to FIG. 18 , each of the data processes is shown providingdivergent data results 1702, which provides a high confidence level thatthe results of the data processes cannot be certified or are otherwiseunreliable (e.g., an erroneous sample analysis).

Referring to FIG. 19 , the data results from the multiple data processesare shown as being mixed between convergent 1700 and divergent dataresults 1702. For instance, three data results are shown as convergentdata results (e.g., data results 1900, 1902, 1904) and two data resultsare shown as divergent data results (e.g., data results 1906, 1908).When results are mixed, such that convergent and divergent data resultsare provided from the multiple data processes, statistical models can beutilized to determine whether there are enough convergent results todiscard or otherwise disregard the divergent results or whether thereare too many divergent results such that there is a low reliability forthe data processes, such as determining whether a data process result isexcluded from a standard deviation analysis of the total results, orthrough another model. For example, a simple majority of convergingresults can indicate a satisfactory reliability of the converging dataresults. In implementations, if a threshold number of divergent dataresults are present, the overall data results can be flagged asunreliable by the system 100. For instance, the system 100 can beconfigured such that if two different data processes are divergent fromthe remainder of the convergent data processes, then the controller 108can identify all of the overall data results as unreliable.Alternatively or additionally, one or more data processes can be used asa benchmark data result and results from one or more additional dataprocesses are compared against the benchmark data result to determinethe scope of deviation from the benchmark.

The process can include reporting the results of the multiple dataprocesses on an automatic basis. For example, the process canautomatically identify and report out which data process(es) provideddata results that were reliable (e.g., converged with other dataresults) or unreliable (e.g., diverged from other data results). Inimplementations, the controller 108 of the system 100 generates one ormore communication signals responsive to generation of data results fromone or more of the data processes. For example, the one or morecommunication signals can be sent to a user interface for review bylaboratory personnel.

Electromechanical devices (e.g., electrical motors, servos, actuators,or the like) may be coupled with or embedded within the components ofthe system 100 to facilitate automated operation via control logicembedded within or externally driving the system 100. Theelectromechanical devices can be configured to cause movement of devicesand fluids according to various procedures, such as the proceduresdescribed herein. The system 100 may include or be controlled by acomputing system having a processor or other controller configured toexecute computer readable program instructions (i.e., the control logic)from a non-transitory carrier medium (e.g., storage medium such as aflash drive, hard disk drive, solid-state disk drive, SD card, opticaldisk, or the like). The computing system can be connected to variouscomponents of the system 100, either by direct connection, or throughone or more network connections (e.g., local area networking (LAN),wireless area networking (WAN or WLAN), one or more hub connections(e.g., USB hubs), and so forth). For example, the computing system canbe communicatively coupled to the system controller, ICP torch, carriagemotors, fluid handling systems (e.g., valves, pumps, etc.), othercomponents described herein, components directing control thereof, orcombinations thereof. The program instructions, when executed by theprocessor or other controller, can cause the computing system to controlthe system 100 according to one or more modes of operation, as describedherein.

It should be recognized that the various functions, control operations,processing blocks, or steps described throughout the present disclosuremay be carried out by any combination of hardware, software, orfirmware. In some embodiments, various steps or functions are carriedout by one or more of the following: electronic circuitry, logic gates,multiplexers, a programmable logic device, an application-specificintegrated circuit (ASIC), a controller/microcontroller, or a computingsystem. A computing system may include, but is not limited to, apersonal computing system, a mobile computing device, mainframecomputing system, workstation, image computer, parallel processor, orany other device known in the art. In general, the term “computingsystem” is broadly defined to encompass any device having one or moreprocessors or other controllers, which execute instructions from acarrier medium.

Program instructions implementing functions, control operations,processing blocks, or steps, such as those manifested by embodimentsdescribed herein, may be transmitted over or stored on carrier medium.The carrier medium may be a transmission medium, such as, but notlimited to, a wire, cable, or wireless transmission link. The carriermedium may also include a non-transitory signal bearing medium orstorage medium such as, but not limited to, a read-only memory, a randomaccess memory, a magnetic or optical disk, a solid-state or flash memorydevice, or a magnetic tape.

CONCLUSION

Although the subject matter has been described in language specific tostructural features and/or process operations, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the specific features or acts described above.Rather, the specific features and acts described above are disclosed asexample forms of implementing the claims.

What is claimed is:
 1. A method for determination of nanoparticledetection factors in fluid samples, comprising: transferring a fluidsample containing nanoparticles to a spectrometry sample analyzer;generating a spectrometry data set via the spectrometry sample analyzerassociated with detected ion signal intensity over time; analyzing thespectrometry data set with a first data process, via one or morecomputer processors, to determine at least one of a first nanoparticlebaseline or a first nanoparticle detection threshold with the first dataprocess; automatically switching to a second data process to analyze,via the one or more computer processors, the spectrometry data set todetermine at least one of a second nanoparticle baseline or a secondnanoparticle detection threshold with the second data process; anddetermining, via the one or more computer processors, whether resultsfrom the first data process converge or diverge from results from thesecond data process.
 2. The method of claim 1, wherein the spectrometrysample analyzer is an inductively coupled plasma mass spectrometer(ICPMS).
 3. The method of claim 2, wherein transferring a fluid samplecontaining nanoparticles to a spectrometry sample analyzer includestransferring the fluid sample from a fluid source to an inductivelycoupled plasma torch and subsequently to the ICPMS.
 4. The method ofclaim 3, wherein transferring the fluid sample from a fluid source to aninductively coupled plasma torch includes transferring the fluid samplefrom the fluid source via autosampler control of a sample probe to theinductively coupled plasma torch.
 5. The method of claim 1, wherein thefirst data process is utilized to determine the first nanoparticlebaseline and wherein the second data process is utilized to determinethe second nanoparticle baseline.
 6. The method of claim 5, furthercomprising automatically switching to a third data process to analyze,via the one or more computer processors, the spectrometry data set todetermine a third nanoparticle baseline; and determining whether resultsfrom the first data process, the second data process, and the third dataprocess converge or diverge.
 7. The method of claim 6, wherein resultsfrom the first data process, the second data process, and the third dataprocess are determined to converge when results from at least two of thefirst data process, the second data process, and the third dataconverge.
 8. The method of claim 1, wherein the first data process isutilized to determine the first nanoparticle detection threshold andwherein the second data process is utilized to determine the secondnanoparticle detection threshold.
 9. The method of claim 8, furthercomprising automatically switching to a third data process to analyze,via the one or more computer processors, the spectrometry data set todetermine a third nanoparticle detection threshold; and determiningwhether results from the first data process, the second data process,and the third data process converge or diverge.
 10. The method of claim9, wherein results from the first data process, the second data process,and the third data process are determined to converge when results fromat least two of the first data process, the second data process, and thethird data converge.
 11. A system for determination of nanoparticledetection factors in fluid samples, comprising: a spectrometry sampleanalyzer configured to receive a fluid sample containing nanoparticlesfrom a sample source and to generate a spectrometry data set associatedwith detected ion signal intensity over time; one or more computerprocessors; and a non-transitory computer readable-medium bearing one ormore instructions for execution by the one or more computer processorsto cause the one or more computer processors to perform the steps of:analyzing the spectrometry data set with a first data process todetermine at least one of a first nanoparticle baseline or a firstnanoparticle detection threshold with the first data process;automatically switching to a second data process to analyze thespectrometry data set to determine at least one of a second nanoparticlebaseline or a second nanoparticle detection threshold with the seconddata process; and determining whether results from the first dataprocess converge or diverge from results from the second data process.12. The system of claim 11, wherein the spectrometry sample analyzer isan inductively coupled plasma mass spectrometer (ICPMS).
 13. The systemof claim 12, further comprising an inductively coupled plasma torchfluidically coupled between the sample source and the ICPMS.
 14. Thesystem of claim 13, further comprising an autosampler directing controlof a sample probe to introduce the fluid sample to the inductivelycoupled plasma torch.
 15. The system of claim 11, wherein the first dataprocess is utilized to determine the first nanoparticle baseline andwherein the second data process is utilized to determine the secondnanoparticle baseline.
 16. The system of claim 15, wherein the one ormore instructions further include one or more instructions for executionby the one or more computer processors to cause the one or more computerprocessors to perform the steps of automatically switching to a thirddata process to analyze the spectrometry data set to determine a thirdnanoparticle baseline; and determining whether results from the firstdata process, the second data process, and the third data processconverge or diverge.
 17. The system of claim 16, wherein results fromthe first data process, the second data process, and the third dataprocess are determined to converge when results from at least two of thefirst data process, the second data process, and the third dataconverge.
 18. The system of claim 11, wherein the first data process isutilized to determine the first nanoparticle detection threshold andwherein the second data process is utilized to determine the secondnanoparticle detection threshold.
 19. The system of claim 18, whereinthe one or more instructions further include one or more instructionsfor execution by the one or more computer processors to cause the one ormore computer processors to perform the steps of automatically switchingto a third data process to analyze the spectrometry data set todetermine a third nanoparticle detection threshold; and determiningwhether results from the first data process, the second data process,and the third data process converge or diverge.
 20. The system of claim19, wherein results from the first data process, the second dataprocess, and the third data process are determined to converge whenresults from at least two of the first data process, the second dataprocess, and the third data converge.